Uncertainty in Artificial Intelligence
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A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring
Ioan Stanculescu, Christopher Williams, Yvonne Freer
Abstract:
In this paper we develop a Hierarchi- cal Switching Linear Dynamical System (HSLDS) for the detection of sepsis in neonates in an intensive care unit. The Fac- torial Switching LDS (FSLDS) of Quinn et al. (2009) is able to describe the observed vital signs data in terms of a number of discrete factors, which have either physiological or ar- tifactual origin. In this paper we demonstrate that by adding a higher-level discrete variable with semantics sepsis/non-sepsis we can de- tect changes in the physiological factors that signal the presence of sepsis. We demonstrate that the performance of our model for the detection of sepsis is not statistically differ- ent from the auto-regressive HMM of Stan- culescu et al. (2013), despite the fact that their model is given ‚??ground truth‚?Ě annota- tions of the physiological factors, while our HSLDS must infer them from the raw vital signs data.
Keywords:
Pages: 752-761
PS Link:
PDF Link: /papers/14/p752-stanculescu.pdf
BibTex:
@INPROCEEDINGS{Stanculescu14,
AUTHOR = "Ioan Stanculescu and Christopher Williams and Yvonne Freer",
TITLE = "A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "752--761"
}


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